What is Business Analytics?

Business Analytics often deals in ‘Big data’ - data sets so large and complex that it is difficult to process them in an acceptable time frame using traditional data processing techniques.

Big data always exhibits one or or more of the “3Vs”:

  • Volume (there is a lot of it)
  • Velocity (it arrives and needs to be processed quickly)
  • Variety (it is derived from disparate sources that may be expressed in a variety of formats)

The growth in data volumes has massively accelerated since 2002

The challenges of big data include its capture, curation, storage, search, sharing, transfer, analysis and visualization. It often requires massively parallel computer architecture for its storage and analysis.

Big data often arises where multiple data sets are analysed together and interrelationships sought rather than the data sets being stored and and analysed separately.

Big data has grown recently as a consequence of the explosion of data capture that has has happened in the last ten years e.g. by satellites, software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks as well as text capture within the worldwide web and in particular within social media. The world's technological per-capita capacity to store information has roughly doubled every three years since the 1980s. As of 2012, every day 2.5 exabytes (2.5×10^18) of data were created. The figure below shows the explosion up to 2007. 


Not only is there a lot of it, but Big Data is often unstructured, i.e. it either does not have a pre-defined data model or it is not organized in a pre-defined manner. Where data is unstructured, the challenges are all the greater due to the inherent irregularities and ambiguities. Text on the worldwide web would be an example of unstructured data. 

Big data occurs in a variety of fields including business informatics, meteorology, genomics, physics, medicine, environmental research, and finance. 

The challenges of big data are great but so too are the opportunities. Successful processing of big data may facilitate decision making, insight discovery and process optimisation and allow, for example, the spotting of business trends, prevention of diseases, combating of crime, and determination of real-time roadway traffic conditions.

Data Analytics

There has been a lot of hype around “big data”, along the lines of: “Big data! If you don’t have it, you better get yourself some. Your competition has it, after all. Bottom line: If your data is little, your rivals are going to kick sand in your face and steal your girlfriend.” 

The truth is that not all organizations will profit from possessing or analysing truly “big data”.  Nevertheless, many could benefit from analysing the data that they do have, even if it is “small”.   This activity might be described by the general terms, “business intelligence” and “data analytics”. 

The UK Government (NESTA) has researched the benefits to businesses of using data to make decisions about the development of products and services:

Of 500 UK companies asked how they make decisions to grow their sales, only 18 per cent use data and analysis. We've called these 18 per cent the Datavores. 

  • Datavores are twice as likely to run controlled experiments to see what works.
  • Datavores are three times as likely to use customer data to develop their business strategy.
  • Datavores are 25 per cent more likely to say they launch products and services before competitors
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Many of the techniques and ideas applicable to big data analytics are also applicable to small data analytics, for example methods for dealing with unstructured data and the value of drawing together disparate data sources to seek patterns and correlations. 

Companies may use the phrase “big data” as a buzzword but we feel the term "data analytics" is often more accurate.  

It should be noted that data analytics does not necessarily require enormous processing power. (Many companies have bought parallel processing “clusters” quite unnecessarily.) 

The value of drawing together disparate data sources from across and outside an organisation to draw business-strategy-level inferences means that it is often a challenge for large enterprises to determine who should own analytics initiatives, but it is clear that the initiatives requires sponsorship at the highest level of the organisation. 

Data analytics draws on statistical and other algorithmic techniques but it also requires insight from those experienced in the field to be analysed. Some correlations deduced by statistics may yield surprising new insight, but some may simply be spurious. Therefore practical knowledge of the field as well as creativity, imagination and common sense are all required in the mix as well as mathematical skill. 

What can it do?

Here are a few examples of the types of questions that data analytics is able to address: 

Business and Marketing
  • What is the best “next purchase” recommendation to present to a particular customer at a particular moment?
  • Which customers is the business likely to lose in the immediate future? 
  • How best may we engage our individual customers via email campaigns without bombarding them? 
  • Why do people unsubscribe from our email shots? 

Government
  • Who is likely to be evading tax? 
  • How is public sentiment changing on certain issues? 

Finance
  • Is a particular person likely to be a bad credit risk? 

Medicine
  • Is a particular person likely to be susceptible to a particular disease?

Third Sector
  • Is a particular young person likely to respond well to one-to-one mentoring?
  • Which community projects improve life skills the most?


How to know if you are big-data ready

Here are our list of qualities demonstrated by “big-data-ready” organisations:

Culture
  • Allowing for trial and error
  • Having the vision to see the opportunities and where it wants to be
  • Understanding the value of current data assets
  • Budgeting geared to the future without immediate expectancy of rewards
  • Buy-in at a senior level

Structure and skill sets
  • Splitting off big data projects from operational IT activities
  • Creation of dedicated teams made up of field experts, business experts, technology experts and business intelligence people
  • Having the key skills to work the data to maximum effect

Integration
  • Having the technology in place to connect internal and external data, including unstructured data
  • Knowedege of data structures, data quality, meta data, open data and other external sources and how to integrate it all
  • Opening up software and data to third parties
  • Collaboration between interested parties (internal & external)
Contact us

Start with a FREE two-hour consultation with one of our top data consultants, and then we can agree how best to proceed.  Contact our consultancy team on 020 7183 1666 or email info@voodoo.co.uk